A hybrid multi-objective immune algorithm for predictive and reactive scheduling

Iwona Paprocka () and Bożena Skołud ()
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Iwona Paprocka: Silesian University of Technology
Bożena Skołud: Silesian University of Technology

Journal of Scheduling, 2017, vol. 20, issue 2, No 4, 165-182

Abstract: Abstract The high productivity of a production process has a major impact on the reduction of the production cost and on a quick response to changing demands. Information about a failure-free machine operation time obtained in advance allows the users to plan preventive maintenance in order to keep the machine in a good operational condition. The introduction of maintenance work into a schedule reduces the frequency of unpredicted breaks caused by machine failures. It also results in higher productivity and in-time production. The foregoing of this constitutes the main idea of the predictive scheduling method proposed in the paper. Rescheduling of disrupted operations, with a minimal impact on the stability and robustness of a schedule, is the main idea of the reactive scheduling method proposed. The first objective of the paper is to present a hybrid multi-objective immune algorithm (H-MOIA) aided by heuristics: a minimal impact of disrupted operation on the schedule (MIDOS) for predictive scheduling and a minimal impact of rescheduled operation on the schedule (MIROS) for reactive scheduling. The second objective is to compare the H-MOIA with various methods for predictive and reactive scheduling. The H-MOIA + MIDOS is compared to two algorithms, identified in reference publications: (1) an algorithm based on priority rules: the least flexible job first (LFJ) and the longest processing time (LPT) (2) an Average Slack Method. The H-MOIA + MIROS is compared to: (1) an algorithm based on priority rules: the LFJ and LPT and (2) Shifted Gap-Reduction. This paper presents the research results and computer simulations.

Keywords: Predictive; Reactive scheduling; Multi-objective optimization; Immune algorithm; Preventive maintenance (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s10951-016-0494-9

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